Analysing Sales Dataset

This dataset is about an electronics store's purchases in 2019.

it contains:

• Order ID (The order unique id)
• Product (The product purchased)
• Quantity Ordered (The product quantity)
• Price Each (Price for each item)
• Order Date (The order date)
• Purchase Address (The address of the order)

In this project, I will answer the following questions:

• What month has the largest sales
• What day has the most sales
• What weekday has the most orders
• What Price Range do people tend to buy more
• Which State, City, and Product has the largest orders?
• What is the most and the least profitable product

Gathering

# Importing libraries
import pandas as pd 
import numpy as np
import seaborn as sb 
import matplotlib.pyplot as plt
import plotly.express as px
import os 
import plotly.offline as pyo
pyo.init_notebook_mode()

# Getting the path of the folder
pwd = os.getcwd()

# Making a list of the files names
files = ['\Sales_January_2019.csv','\Sales_February_2019.csv','\Sales_March_2019.csv','\Sales_April_2019.csv','\Sales_May_2019.csv','\Sales_June_2019.csv','\Sales_July_2019.csv','\Sales_August_2019.csv',
 '\Sales_September_2019.csv', '\Sales_October_2019.csv', '\Sales_November_2019.csv','\Sales_December_2019.csv']

# create empty list to store the data in 
Each_month = []
# append datasets into the list
for i in range(len(files)):
    Each_month.append(pd.read_csv(pwd+"\Data"+files[i]))

Each_month[0]

	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
0	141234	iPhone	1	700	01/22/19 21:25	944 Walnut St, Boston, MA 02215
1	141235	Lightning Charging Cable	1	14.95	01/28/19 14:15	185 Maple St, Portland, OR 97035
2	141236	Wired Headphones	2	11.99	01/17/19 13:33	538 Adams St, San Francisco, CA 94016
3	141237	27in FHD Monitor	1	149.99	01/05/19 20:33	738 10th St, Los Angeles, CA 90001
4	141238	Wired Headphones	1	11.99	01/25/19 11:59	387 10th St, Austin, TX 73301
...	...	...	...	...	...	...
9718	150497	20in Monitor	1	109.99	01/26/19 19:09	95 8th St, Dallas, TX 75001
9719	150498	27in FHD Monitor	1	149.99	01/10/19 22:58	403 7th St, San Francisco, CA 94016
9720	150499	ThinkPad Laptop	1	999.99	01/21/19 14:31	214 Main St, Portland, OR 97035
9721	150500	AAA Batteries (4-pack)	2	2.99	01/15/19 14:21	810 2nd St, Los Angeles, CA 90001
9722	150501	Google Phone	1	600	01/13/19 16:43	428 Cedar St, Boston, MA 02215

9723 rows × 6 columns

# Combining the Tables into one table 
df = pd.concat([Each_month[0], Each_month[1], Each_month[2], Each_month[3], Each_month[4], Each_month[5], Each_month[6], Each_month[7], Each_month[8], Each_month[9], Each_month[10], Each_month[11]], ignore_index=True)

Assessing

df

	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
0	141234	iPhone	1	700	01/22/19 21:25	944 Walnut St, Boston, MA 02215
1	141235	Lightning Charging Cable	1	14.95	01/28/19 14:15	185 Maple St, Portland, OR 97035
2	141236	Wired Headphones	2	11.99	01/17/19 13:33	538 Adams St, San Francisco, CA 94016
3	141237	27in FHD Monitor	1	149.99	01/05/19 20:33	738 10th St, Los Angeles, CA 90001
4	141238	Wired Headphones	1	11.99	01/25/19 11:59	387 10th St, Austin, TX 73301
...	...	...	...	...	...	...
186845	319666	Lightning Charging Cable	1	14.95	12/11/19 20:58	14 Madison St, San Francisco, CA 94016
186846	319667	AA Batteries (4-pack)	2	3.84	12/01/19 12:01	549 Willow St, Los Angeles, CA 90001
186847	319668	Vareebadd Phone	1	400	12/09/19 06:43	273 Wilson St, Seattle, WA 98101
186848	319669	Wired Headphones	1	11.99	12/03/19 10:39	778 River St, Dallas, TX 75001
186849	319670	Bose SoundSport Headphones	1	99.99	12/21/19 21:45	747 Chestnut St, Los Angeles, CA 90001

186850 rows × 6 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 186850 entries, 0 to 186849
Data columns (total 6 columns):
 #   Column            Non-Null Count   Dtype 
---  ------            --------------   ----- 
 0   Order ID          186305 non-null  object
 1   Product           186305 non-null  object
 2   Quantity Ordered  186305 non-null  object
 3   Price Each        186305 non-null  object
 4   Order Date        186305 non-null  object
 5   Purchase Address  186305 non-null  object
dtypes: object(6)
memory usage: 8.6+ MB

# Checking the unique values of the product columns
# And found that the header in repeated as a record
df.Product.value_counts()

USB-C Charging Cable          21903
Lightning Charging Cable      21658
AAA Batteries (4-pack)        20641
AA Batteries (4-pack)         20577
Wired Headphones              18882
Apple Airpods Headphones      15549
Bose SoundSport Headphones    13325
27in FHD Monitor               7507
iPhone                         6842
27in 4K Gaming Monitor         6230
34in Ultrawide Monitor         6181
Google Phone                   5525
Flatscreen TV                  4800
Macbook Pro Laptop             4724
ThinkPad Laptop                4128
20in Monitor                   4101
Vareebadd Phone                2065
LG Washing Machine              666
LG Dryer                        646
Product                         355
Name: Product, dtype: int64

# Showing the records with the problem
df.query("Product == 'Product'")

	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
1073	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
1102	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
1194	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
1897	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
2463	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
...	...	...	...	...	...	...
184931	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
185070	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
185481	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
185925	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
185955	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address

355 rows × 6 columns

# Checking the NULLs 
df.isnull().sum()

Order ID            545
Product             545
Quantity Ordered    545
Price Each          545
Order Date          545
Purchase Address    545
dtype: int64

# Checking Duplicates
pd.concat(g for _, g in df.groupby("Order ID") if len(g) > 1)

	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
41	141275	USB-C Charging Cable	1	11.95	01/07/19 16:06	610 Walnut St, Austin, TX 73301
42	141275	Wired Headphones	1	11.99	01/07/19 16:06	610 Walnut St, Austin, TX 73301
57	141290	Apple Airpods Headphones	1	150	01/02/19 08:25	4 1st St, Los Angeles, CA 90001
58	141290	AA Batteries (4-pack)	3	3.84	01/02/19 08:25	4 1st St, Los Angeles, CA 90001
133	141365	Vareebadd Phone	1	400	01/10/19 11:19	20 Dogwood St, New York City, NY 10001
...	...	...	...	...	...	...
184931	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
185070	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
185481	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
185925	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
185955	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address

15004 rows × 6 columns

Issues:

• Wrong data types for some columns
• The columns headers are repeated within the data
• Columns with two words need to be changed
• There is NULL Values

Cleaning

# making a copy from the data 
df_modified = df.copy()

• The columns headers are repeated within the data

df_modified = df_modified.query("Product != 'Product'")

df_modified.query("Product == 'Product'")

	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address

• Wrong data types for some columns

# Converting each column's type to its correct type
# but will leave order date with a string datatype to be able to convert it later
df_modified['Quantity Ordered'] = pd.to_numeric(df_modified['Quantity Ordered'])
df_modified['Price Each'] = pd.to_numeric(df_modified['Price Each'])

df_modified.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 186495 entries, 0 to 186849
Data columns (total 6 columns):
 #   Column            Non-Null Count   Dtype  
---  ------            --------------   -----  
 0   Order ID          185950 non-null  object 
 1   Product           185950 non-null  object 
 2   Quantity Ordered  185950 non-null  float64
 3   Price Each        185950 non-null  float64
 4   Order Date        185950 non-null  object 
 5   Purchase Address  185950 non-null  object 
dtypes: float64(2), object(4)
memory usage: 10.0+ MB

• Columns with two words needs to be changed

# Changing some columns name to be efficient in python
df_modified.rename(columns={'Order ID': 'Order_ID', 'Quantity Ordered': 'Quantity_Ordered', 'Price Each':'Price_Each', 'Order Date': 'Order_Date', 'Purchase Address': 'Purchase_Address'}, inplace=True)

df_modified.columns

Index(['Order_ID', 'Product', 'Quantity_Ordered', 'Price_Each', 'Order_Date',
       'Purchase_Address'],
      dtype='object')

• There is NULL values

# Dropping the NULLs
df_modified.dropna(inplace=True)

# Checking the NULLs after removing it
df_modified.isnull().sum()

Order_ID            0
Product             0
Quantity_Ordered    0
Price_Each          0
Order_Date          0
Purchase_Address    0
dtype: int64

# Extracting the date only from the Order_Date column 
df_modified['Date'] = df_modified['Order_Date'].apply(lambda x : x.split()[0])

#Extracting the Day from the Date column 
df_modified['Day'] = df_modified['Date'].apply(lambda x : x.split("/")[1])

df_modified.head(2)

	Order_ID	Product	Quantity_Ordered	Price_Each	Order_Date	Purchase_Address	Date	Day
0	141234	iPhone	1.0	700.00	01/22/19 21:25	944 Walnut St, Boston, MA 02215	01/22/19	22
1	141235	Lightning Charging Cable	1.0	14.95	01/28/19 14:15	185 Maple St, Portland, OR 97035	01/28/19	28

# Convert the Date datatype column to a datetime 
df_modified['Date'] = pd.to_datetime(df_modified['Date'])

# Extracting the month and weekday from the date 
df_modified['Month'] = pd.DatetimeIndex(df_modified['Date']).month
df_modified['DayOfWeek'] = df_modified['Date'].dt.day_name()

# Checking 
df_modified.info()
df_modified

<class 'pandas.core.frame.DataFrame'>
Int64Index: 185950 entries, 0 to 186849
Data columns (total 10 columns):
 #   Column            Non-Null Count   Dtype         
---  ------            --------------   -----         
 0   Order_ID          185950 non-null  object        
 1   Product           185950 non-null  object        
 2   Quantity_Ordered  185950 non-null  float64       
 3   Price_Each        185950 non-null  float64       
 4   Order_Date        185950 non-null  object        
 5   Purchase_Address  185950 non-null  object        
 6   Date              185950 non-null  datetime64[ns]
 7   Day               185950 non-null  object        
 8   Month             185950 non-null  int64         
 9   DayOfWeek         185950 non-null  object        
dtypes: datetime64[ns](1), float64(2), int64(1), object(6)
memory usage: 15.6+ MB

	Order_ID	Product	Quantity_Ordered	Price_Each	Order_Date	Purchase_Address	Date	Day	Month	DayOfWeek
0	141234	iPhone	1.0	700.00	01/22/19 21:25	944 Walnut St, Boston, MA 02215	2019-01-22	22	1	Tuesday
1	141235	Lightning Charging Cable	1.0	14.95	01/28/19 14:15	185 Maple St, Portland, OR 97035	2019-01-28	28	1	Monday
2	141236	Wired Headphones	2.0	11.99	01/17/19 13:33	538 Adams St, San Francisco, CA 94016	2019-01-17	17	1	Thursday
3	141237	27in FHD Monitor	1.0	149.99	01/05/19 20:33	738 10th St, Los Angeles, CA 90001	2019-01-05	05	1	Saturday
4	141238	Wired Headphones	1.0	11.99	01/25/19 11:59	387 10th St, Austin, TX 73301	2019-01-25	25	1	Friday
...	...	...	...	...	...	...	...	...	...	...
186845	319666	Lightning Charging Cable	1.0	14.95	12/11/19 20:58	14 Madison St, San Francisco, CA 94016	2019-12-11	11	12	Wednesday
186846	319667	AA Batteries (4-pack)	2.0	3.84	12/01/19 12:01	549 Willow St, Los Angeles, CA 90001	2019-12-01	01	12	Sunday
186847	319668	Vareebadd Phone	1.0	400.00	12/09/19 06:43	273 Wilson St, Seattle, WA 98101	2019-12-09	09	12	Monday
186848	319669	Wired Headphones	1.0	11.99	12/03/19 10:39	778 River St, Dallas, TX 75001	2019-12-03	03	12	Tuesday
186849	319670	Bose SoundSport Headphones	1.0	99.99	12/21/19 21:45	747 Chestnut St, Los Angeles, CA 90001	2019-12-21	21	12	Saturday

185950 rows × 10 columns

# Converting the month from the month number to the month name
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
months_num = [1,2,3,4,5,6,7,8,9,10,11,12]
for n in months_num:
    df_modified['Month'][df_modified['Month'] == n] = months[n-1]
df_modified

<ipython-input-76-e9540837e5e1>:5: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

	Order_ID	Product	Quantity_Ordered	Price_Each	Order_Date	Purchase_Address	Date	Day	Month	DayOfWeek
0	141234	iPhone	1.0	700.00	01/22/19 21:25	944 Walnut St, Boston, MA 02215	2019-01-22	22	Jan	Tuesday
1	141235	Lightning Charging Cable	1.0	14.95	01/28/19 14:15	185 Maple St, Portland, OR 97035	2019-01-28	28	Jan	Monday
2	141236	Wired Headphones	2.0	11.99	01/17/19 13:33	538 Adams St, San Francisco, CA 94016	2019-01-17	17	Jan	Thursday
3	141237	27in FHD Monitor	1.0	149.99	01/05/19 20:33	738 10th St, Los Angeles, CA 90001	2019-01-05	05	Jan	Saturday
4	141238	Wired Headphones	1.0	11.99	01/25/19 11:59	387 10th St, Austin, TX 73301	2019-01-25	25	Jan	Friday
...	...	...	...	...	...	...	...	...	...	...
186845	319666	Lightning Charging Cable	1.0	14.95	12/11/19 20:58	14 Madison St, San Francisco, CA 94016	2019-12-11	11	Dec	Wednesday
186846	319667	AA Batteries (4-pack)	2.0	3.84	12/01/19 12:01	549 Willow St, Los Angeles, CA 90001	2019-12-01	01	Dec	Sunday
186847	319668	Vareebadd Phone	1.0	400.00	12/09/19 06:43	273 Wilson St, Seattle, WA 98101	2019-12-09	09	Dec	Monday
186848	319669	Wired Headphones	1.0	11.99	12/03/19 10:39	778 River St, Dallas, TX 75001	2019-12-03	03	Dec	Tuesday
186849	319670	Bose SoundSport Headphones	1.0	99.99	12/21/19 21:45	747 Chestnut St, Los Angeles, CA 90001	2019-12-21	21	Dec	Saturday

185950 rows × 10 columns

# Checking
df_modified.Month.value_counts().sort_values()

Jan     9709
Sep    11621
Aug    11961
Feb    11975
Jun    13554
Jul    14293
Mar    15153
May    16566
Nov    17573
Apr    18279
Oct    20282
Dec    24984
Name: Month, dtype: int64

# Extracting the city and state from the address 
df_modified['City'] = df_modified['Purchase_Address'].apply(lambda x : x.split(',')[1])
df_modified['State'] = df_modified['Purchase_Address'].apply(lambda x : x.split(',')[2])
df_modified['State'] = df_modified['State'].apply(lambda x : x.split()[0])
df_modified

	Order_ID	Product	Quantity_Ordered	Price_Each	Order_Date	Purchase_Address	Date	Day	Month	DayOfWeek	City	State
0	141234	iPhone	1.0	700.00	01/22/19 21:25	944 Walnut St, Boston, MA 02215	2019-01-22	22	Jan	Tuesday	Boston	MA
1	141235	Lightning Charging Cable	1.0	14.95	01/28/19 14:15	185 Maple St, Portland, OR 97035	2019-01-28	28	Jan	Monday	Portland	OR
2	141236	Wired Headphones	2.0	11.99	01/17/19 13:33	538 Adams St, San Francisco, CA 94016	2019-01-17	17	Jan	Thursday	San Francisco	CA
3	141237	27in FHD Monitor	1.0	149.99	01/05/19 20:33	738 10th St, Los Angeles, CA 90001	2019-01-05	05	Jan	Saturday	Los Angeles	CA
4	141238	Wired Headphones	1.0	11.99	01/25/19 11:59	387 10th St, Austin, TX 73301	2019-01-25	25	Jan	Friday	Austin	TX
...	...	...	...	...	...	...	...	...	...	...	...	...
186845	319666	Lightning Charging Cable	1.0	14.95	12/11/19 20:58	14 Madison St, San Francisco, CA 94016	2019-12-11	11	Dec	Wednesday	San Francisco	CA
186846	319667	AA Batteries (4-pack)	2.0	3.84	12/01/19 12:01	549 Willow St, Los Angeles, CA 90001	2019-12-01	01	Dec	Sunday	Los Angeles	CA
186847	319668	Vareebadd Phone	1.0	400.00	12/09/19 06:43	273 Wilson St, Seattle, WA 98101	2019-12-09	09	Dec	Monday	Seattle	WA
186848	319669	Wired Headphones	1.0	11.99	12/03/19 10:39	778 River St, Dallas, TX 75001	2019-12-03	03	Dec	Tuesday	Dallas	TX
186849	319670	Bose SoundSport Headphones	1.0	99.99	12/21/19 21:45	747 Chestnut St, Los Angeles, CA 90001	2019-12-21	21	Dec	Saturday	Los Angeles	CA

185950 rows × 12 columns

df_modified['State'].value_counts()

CA    74337
NY    24876
TX    24725
MA    19934
GA    14881
WA    14732
OR    10010
ME     2455
Name: State, dtype: int64

# Getting the total price for each order
df_modified['Total_Price'] = df_modified['Quantity_Ordered']* df_modified['Price_Each']
df_modified

	Order_ID	Product	Quantity_Ordered	Price_Each	Order_Date	Purchase_Address	Date	Day	Month	DayOfWeek	City	State	Total_Price
0	141234	iPhone	1.0	700.00	01/22/19 21:25	944 Walnut St, Boston, MA 02215	2019-01-22	22	Jan	Tuesday	Boston	MA	700.00
1	141235	Lightning Charging Cable	1.0	14.95	01/28/19 14:15	185 Maple St, Portland, OR 97035	2019-01-28	28	Jan	Monday	Portland	OR	14.95
2	141236	Wired Headphones	2.0	11.99	01/17/19 13:33	538 Adams St, San Francisco, CA 94016	2019-01-17	17	Jan	Thursday	San Francisco	CA	23.98
3	141237	27in FHD Monitor	1.0	149.99	01/05/19 20:33	738 10th St, Los Angeles, CA 90001	2019-01-05	05	Jan	Saturday	Los Angeles	CA	149.99
4	141238	Wired Headphones	1.0	11.99	01/25/19 11:59	387 10th St, Austin, TX 73301	2019-01-25	25	Jan	Friday	Austin	TX	11.99
...	...	...	...	...	...	...	...	...	...	...	...	...	...
186845	319666	Lightning Charging Cable	1.0	14.95	12/11/19 20:58	14 Madison St, San Francisco, CA 94016	2019-12-11	11	Dec	Wednesday	San Francisco	CA	14.95
186846	319667	AA Batteries (4-pack)	2.0	3.84	12/01/19 12:01	549 Willow St, Los Angeles, CA 90001	2019-12-01	01	Dec	Sunday	Los Angeles	CA	7.68
186847	319668	Vareebadd Phone	1.0	400.00	12/09/19 06:43	273 Wilson St, Seattle, WA 98101	2019-12-09	09	Dec	Monday	Seattle	WA	400.00
186848	319669	Wired Headphones	1.0	11.99	12/03/19 10:39	778 River St, Dallas, TX 75001	2019-12-03	03	Dec	Tuesday	Dallas	TX	11.99
186849	319670	Bose SoundSport Headphones	1.0	99.99	12/21/19 21:45	747 Chestnut St, Los Angeles, CA 90001	2019-12-21	21	Dec	Saturday	Los Angeles	CA	99.99

185950 rows × 13 columns

# Dropping order_date and address columns 
df_modified.drop(columns=['Order_Date', 'Purchase_Address', 'Date'], inplace=True)
df_modified

	Order_ID	Product	Quantity_Ordered	Price_Each	Day	Month	DayOfWeek	City	State	Total_Price
0	141234	iPhone	1.0	700.00	22	Jan	Tuesday	Boston	MA	700.00
1	141235	Lightning Charging Cable	1.0	14.95	28	Jan	Monday	Portland	OR	14.95
2	141236	Wired Headphones	2.0	11.99	17	Jan	Thursday	San Francisco	CA	23.98
3	141237	27in FHD Monitor	1.0	149.99	05	Jan	Saturday	Los Angeles	CA	149.99
4	141238	Wired Headphones	1.0	11.99	25	Jan	Friday	Austin	TX	11.99
...	...	...	...	...	...	...	...	...	...	...
186845	319666	Lightning Charging Cable	1.0	14.95	11	Dec	Wednesday	San Francisco	CA	14.95
186846	319667	AA Batteries (4-pack)	2.0	3.84	01	Dec	Sunday	Los Angeles	CA	7.68
186847	319668	Vareebadd Phone	1.0	400.00	09	Dec	Monday	Seattle	WA	400.00
186848	319669	Wired Headphones	1.0	11.99	03	Dec	Tuesday	Dallas	TX	11.99
186849	319670	Bose SoundSport Headphones	1.0	99.99	21	Dec	Saturday	Los Angeles	CA	99.99

185950 rows × 10 columns

Exploratory Analysis

1) What month has the largest sales

df_modified.groupby('Month')['Total_Price'].sum().sort_values(ascending=False).plot(kind = 'bar', figsize=(12,6))
plt.ylabel('Total sales by $Millions', fontsize=14)
plt.xlabel('Months', fontsize=14)
plt.xticks(rotation=0)
plt.title('Total Sales Per Month', fontsize=18)
print("The month with the largest sales is December")

The month with the largest sales is December

2) What day has the most orders

df_modified.groupby('Day')['Quantity_Ordered'].sum().plot(kind = 'bar', figsize=(12,6))
plt.ylabel('Number of orders', fontsize=14)
plt.xlabel('Day', fontsize=14)
plt.xticks(rotation=0)
plt.title('Total orders per day', fontsize=18)
print("There is no different between the days in orders numbers.")

There is no different between the days in orders numbers.

3) What weekday has the most orders

df_modified.groupby('DayOfWeek')['Quantity_Ordered'].sum().sort_values(ascending=False).plot(kind = 'bar', figsize=(12,6))
plt.ylabel('Number of orders', fontsize=14)
plt.xlabel('Weekday', fontsize=14)
plt.xticks(rotation=0)
plt.title('Total orders per weekday', fontsize=18)
print("There is no different between the weekdays.")

There is no different between the weekdays.

4) What Price Range poeple tend to buy more?

df_modified.Price_Each.hist(figsize=(12,6), grid = False);
plt.xlabel('Prices Ranges', fontsize=14);
plt.ylabel('Frequency', fontsize=14);
plt.title('The Range of prices people tend to buy', fontsize=18)
print("It looks like people like to buy cheaper products more than the expensive ones")

It looks like people like to buy cheaper products more than the expensive ones

5) Which State, City, and Product has the largest orders?

fig = px.sunburst(df_modified, path =["State","City", "Product"], values = "Quantity_Ordered", width = 1000, height= 800, color_continuous_scale='Teal', color = "Quantity_Ordered")
print("California is the State with the biggest orders\nSan Francisco is the City with the biggest orders\nBatteries are the most requested product")
fig.show()

California is the State with the biggest orders
San Francisco is the City with the biggest orders
Batteries are the most requested product

6) What is the most profitable product

df_modified.groupby('Product')['Total_Price'].sum().sort_values().plot(kind='barh',fontsize = 14, figsize=(8,8))
plt.ylabel('Product', fontsize=14)
plt.xlabel('Total Sales by $Millions', fontsize=14)
plt.title('Total Sales per Product', fontsize=18)
print("Despite the Batteries are the most product to be sold, \nThe Macbook Pro Laptop is the most profitable product and the Batteries is the least profitable Products.")

Despite the Batteries are the most product to be sold, 
The Macbook Pro Laptop is the most profitable product and the Batteries is the least profitable Products.

# Saving the final dataset to excel file
df_modified.to_excel('FinalSales.xlsx', index=False)